Introducing the Adaptive U.S. Factor ETF (AUSF)

On August 28th, the Global X Adaptive U.S. Factor ETF (AUSF) began trading on the NYSE. The fund tracks the Adaptive Wealth Strategies U.S. Factor index, which seeks to outperform traditional cap-weighted indexes by employing a dynamic multi-factor investment strategy that allocates across three factors: minimum volatility, value, and momentum. To discuss the strategy in greater detail, we interviewed Kris Carroll of Adaptive Wealth Strategies, the index provider for AUSF.

Can you give an overview of the index that AUSF tracks, the Adaptive Wealth Strategies U.S. Factor Index?

Sure. Our index dynamically allocates across three investment factors: minimum volatility, value, and momentum, based on a mean-reversion approach. Each of these underlying factors have academically sound investment philosophies for their persistence and have historically demonstrated performance advantages compared to traditional market cap weighted indexes. By allocating across these factors, the index seeks to mitigate downside risks, while maintaining the potential for alpha generation.

Why did you create this index?

Our Founder and CEO Larry Carroll instilled the idea that “the best interest of the client is the only interest that matters.” While our investment philosophy has remained consistent over the years, we have delved into a multitude of products and strategies, striving to find what is best for our clients. Through those products, we’ve experienced instances of capital gains distributions from mutual funds, large tracking-error from active management, and zero-alpha from purely passive vehicles.

When single-factor ETFs became readily available, we recognized that these were useful tools for efficiently shifting between minimum volatility, value, and momentum. After using these single-factor ETFs for years, we realized our large asset base gave us a unique opportunity: we could migrate our strategy to a rules-based index and work with an ETF sponsor like Global X to track this index with an ETF. By investing in one multi-factor ETF rather than three separate single-factor ETFs, we expect to reduce turnover and capital gains that often result when selling one factor ETF for another. This is what inspired the creation of the Adaptive Wealth Strategies US Factor Index.

Our goal from the beginning has been to design a low-cost, tax-efficient strategy with the potential for alpha for our clients. We built this index with a rules-based approach that emulates similar strategies that we have been using in client portfolios for years. It was designed by advisors and portfolio managers to use inside real client accounts. The strategy was designed to be understood by both the advisor and the end-client.

How does this index work?

We took the factors which we have already discussed: minimum volatility, value, and momentum, and analyzed how they have historically performed compared to each other. As a result of this analysis, we developed a methodology that will either allocate to two factors at one time, equally weighted 50% / 50%; or to all three with a weighting of 40% / 40% / 20%. The methodology looks to eliminate or underweight the best performing factor over a specified trailing time period.

Why eliminate the best performing factor? This hits at the heart of our philosophy: mean-reversion. Factors are cyclical, and factor performance can become stretched. Many advisors can point to times when momentum became too far stretched to the upside after the investment herd bought in. What happened next? Often, it mean-reverted after spending time at the top and came crashing back down. The same can be said of value as well; its performance can become stretched before mean-reverting. Our goal is to rotate to the factors that are overly stretched on the downside and avoid the ones that are overly stretched on the upside. This provides the opportunity for the underperforming factors to appreciate and hopefully avoid the factor that is about to fall. While not foolproof, it is an intuitive strategy that attempts to follow a basic investment principal: buy low and sell high.

While developing the strategy, we also knew that we did not want to re-optimize the portfolio to look like the market-cap weighted index. This means we did not overlay a sector-neutral strategy or implement other complex constraints. The goal was to let the factor selection and the mean-reversion of the factors drive the returns. Given that the strategy owns, at a minimum, two factors at any point in time, we expect it to avoid the sector concentration that may occur from only owning one single factor.

Yes, we could have applied some of these constraints and tried to over-engineer the strategy. This over-engineering could have lowered the tracking error, but in our opinion also would have also lowered the alpha. If the goal is to outperform something over time, we know it becomes more difficult to achieve this with additional constraints.

How do you think this strategy could be used in a portfolio?

We believe this strategy can serve as the core U.S. Large Cap Equity allocation in a portfolio. This would typically represent anywhere from 70-100% of the U.S. Equity allocation within an account. For smaller accounts we believe it could represent a higher percentage of the U.S. Equity allocation, whereas for larger accounts, it could be the heart of the U.S. Equity allocation, and satellite it with smaller positions that are more thematic and have higher tracking error to the benchmark.

Clearly, there are multiple ways advisors can use this strategy. A common implementation we have seen is using it as portion of the U.S. Equity allocation. If active managers are serving as the core of your portfolio, this can easily be paired with them with the goal of potentially lowering tracking error and fees. If you are using purely passive vehicles, the inclusion of this strategy has the potential to generate alpha and limit drawdowns. It can also be paired with other smart-beta strategies that blend factors differently or are only focused on single factors, as our adaptive factor strategy not designed the same.

Related ETFs

AUSF: The Global X Adaptive U.S. Factor ETF track an index which seeks to outperform traditional cap-weighted indexes by employing a dynamic multi-factor investment strategy that allocates across three factors: minimum volatility, value, and momentum.